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#!/usr/bin/env python3
"""Audit the same-path five-seed contrastive-bias confirmation."""
import argparse
import json
import math
from pathlib import Path
import statistics
from contrastive_bias_c1 import CONDITIONS, RESULT_ROOT, SEEDS
T95 = 2.131846786326649
def read_json(path):
with open(path, encoding="utf-8") as handle:
return json.load(handle)
def record_path(seed, condition):
name = f"dp-bias-c1-s{seed}-{condition.replace('_', '-')}"
return RESULT_ROOT / (name + ".json")
def bound(values, absolute_mean=False):
mean = statistics.fmean(values)
sem = statistics.stdev(values) / math.sqrt(len(values))
center = abs(mean) if absolute_mean else mean
return {"mean": mean, "sem": sem, "upper_95": center + T95 * sem}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--out", type=Path,
default=RESULT_ROOT.parent / "c1_gate.json")
args = parser.parse_args()
conditions = tuple(row[0] for row in CONDITIONS)
missing = [
f"s{seed}:{condition}" for seed in SEEDS for condition in conditions
if not record_path(seed, condition).is_file()
]
if missing:
raise RuntimeError("missing C1 cells: " + ", ".join(missing))
records = {}
for seed in SEEDS:
for condition in conditions:
record = read_json(record_path(seed, condition))
if (
record.get("status") != "completed"
or record.get("seed") != seed
or record.get("condition") != condition
or record.get("ratio") != 4.0
or not math.isnan(float(
(record.get("history") or {}).get("test_accuracy", float("nan"))))
):
raise RuntimeError(f"invalid C1 record s{seed}:{condition}")
records[(seed, condition)] = record
rows = []
gains = []
deficits = []
innovation_oracle = []
for seed in SEEDS:
histories = {
condition: records[(seed, condition)]["history"]
for condition in conditions
}
clean = histories["same_path_clean"]["final_validation_accuracy"]
raw = histories["raw"]["final_validation_accuracy"]
innovation = histories["innovation"]["final_validation_accuracy"]
oracle = histories["oracle"]["final_validation_accuracy"]
raw_failed = (
histories["raw"]["finite"] is not True or clean - raw >= 20.0)
gains.append(innovation - raw)
deficits.append(clean - innovation)
innovation_oracle.append(innovation - oracle)
uuids = {
records[(seed, condition)]["hardware"]["uuid"]
for condition in conditions
}
rows.append({
"seed": seed, "same_path_clean": clean, "raw": raw,
"innovation": innovation, "oracle": oracle,
"raw_finite": histories["raw"]["finite"],
"raw_failed": raw_failed, "innovation_minus_raw": innovation - raw,
"clean_minus_innovation": clean - innovation,
"innovation_minus_oracle": innovation - oracle,
"single_physical_gpu": len(uuids) == 1,
"physical_gpu_uuid": next(iter(uuids)) if len(uuids) == 1 else None,
})
gain_stats = bound(gains)
gain_stats["lower_95"] = gain_stats["mean"] - T95 * gain_stats["sem"]
deficit_stats = bound(deficits)
oracle_stats = bound(innovation_oracle, absolute_mean=True)
complete_finite = all(
records[(seed, condition)]["history"]["finite"] is True
and records[(seed, condition)]["history"]["epochs_completed"] == 130
for seed in SEEDS
for condition in ("same_path_clean", "innovation", "oracle")
)
clean_mean = statistics.fmean(row["same_path_clean"] for row in rows)
post_bias_max = max(
max(abs(value) for value in records[(seed, "innovation")]["history"]
["curves"]["post_bias_raw_bias_rms_ratio"])
for seed in SEEDS
)
instruction_max = max(
max(abs(value) for value in records[(seed, condition)]["history"]
["curves"]["instruction_observations_for_predictor"])
for seed in SEEDS for condition in conditions
)
source_locks = {
json.dumps(record["source"], sort_keys=True) for record in records.values()
}
registry_locks = {record["registry_sha256"] for record in records.values()}
checks = {
"complete_finite_clean_innovation_oracle": complete_finite,
"mean_same_path_clean_at_least_80": clean_mean >= 80.0,
"raw_failed_every_seed": all(row["raw_failed"] for row in rows),
"innovation_above_raw_every_seed": all(value > 0 for value in gains),
"gain_lower_95_above_20": gain_stats["lower_95"] > 20.0,
"clean_deficit_upper_95_below_2": deficit_stats["upper_95"] < 2.0,
"innovation_oracle_abs_mean_bound_below_2": oracle_stats["upper_95"] < 2.0,
"post_bias_ratio_at_most_1e_3": post_bias_max <= 1e-3,
"zero_instruction_observations": instruction_max == 0.0,
"same_gpu_within_every_seed": all(row["single_physical_gpu"] for row in rows),
"single_source_lock": len(source_locks) == 1,
"single_registry_lock": len(registry_locks) == 1,
}
first = records[(SEEDS[0], conditions[0])]
report = {
"stage": "contrastive_bias_c1", "gate": (
"pass" if all(checks.values()) else "fail"),
"checks": checks, "rows": rows,
"mean_same_path_clean_validation_accuracy": clean_mean,
"paired_innovation_minus_raw": gain_stats,
"paired_clean_minus_innovation": deficit_stats,
"paired_innovation_minus_oracle": oracle_stats,
"maximum_post_bias_ratio": post_bias_max,
"maximum_instruction_observations": instruction_max,
"num_expected_records": 20, "num_audited_records": len(records),
"source": first["source"], "registry_sha256": first["registry_sha256"],
"test_policy": "none",
}
args.out.parent.mkdir(parents=True, exist_ok=True)
with open(args.out, "w", encoding="utf-8") as handle:
json.dump(report, handle, indent=2, sort_keys=True)
handle.write("\n")
print(json.dumps(report, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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